Characterization of dynamic interactions between cardiovascular signals by time-frequency coherence

Identification of patients prone to hypotension during hemodialysis based on the analysis of cardiovascular signals

Intradialytic hypotension (IDH) is a major complication during hemodialysis treatment, and therefore it is highly desirable to identify, at an early stage during treatment, whether the patient is prone to IDH. Heart rate variability (HRV), blood pressure variability (BPV) and baroreflex sensitivity (BRS) were analyzed during the first 30 min of treatment to assess information on the autonomic nervous system. Using the sequential floating forward selection method and linear classification, the set of features with the best discriminative power was selected, resulting in an accuracy of 92.1%. Using a classifier based on the HRV features only, thereby avoiding that continuous blood pressure has to be recorded, accuracy decreased to 90.2%. The results suggest that an HRV-based classifier is useful for determining whether a patient is prone to IDH at the beginning of the treatment.

Recursive identification of an arterial baroreflex model for the evaluation of cardiovascular autonomic modulation

The evaluation of the time-varying vagal and sympathetic contributions to heart rate remains a challenging task because the observability of the baroreflex is generally limited and the time-varying properties are difficult to take into account, especially in non-stationnary conditions. The objective is to propose a model-based approach to estimate the autonomic modulation during a pharmacological challenge. A recursive parameter identification method is proposed and applied to a mathematical model of the baroreflex, in order to estimate the time-varying vagal and sympathetic contributions to heart rate modulation during autonomic maneuvers. The model-based method was evaluated with data from five newborn lambs, which were acquired during injection of vasodilator and vasoconstrictor drugs, on normal conditions and under beta-blockers, so as to quantify the effect of the pharmacological sympathetic blockade on the estimated parameters. After parameter identification, results show a close match between experimental and simulated signals for the five lambs, as the mean relative root mean squared error is equal to 0.0026 (± 0.003). The error, between simulated and experimental signals, is significantly reduced compared to a batch identification of parameters. The model-based estimation of vagal and sympathetic contributions were consistent with physiological knowledge and, as expected, it was possible to observe an alteration of the sympathetic response under beta-blockers. The simulated vagal modulation illustrates a response similar to traditional heart rate variability markers during the pharmacological maneuver. The model-based method, proposed in the paper, highlights the advantages of using a recursive identification method for the estimation of vagal and sympathetic modulation.

A new approach for the quantification of synchrony of multivariate non-stationary psychophysiological variables during emotion eliciting stimuli

Emotion eliciting situations are accompanied by changes of multiple variables associated with subjective, physiological and behavioral responses. The quantification of the overall simultaneous synchrony of psychophysiological reactions plays a major role in emotion theories and has received increased attention in recent years. From a psychometric perspective, the reactions represent multivariate non-stationary intra-individual time series. In this paper, a new time-frequency based latent variable approach for the quantification of the synchrony of the responses is presented. The approach is applied to empirical data, collected during an emotion eliciting situation. The results are compared with a complementary inter-individual approach of Hsieh et al. (2011). Finally, the proposed approach is discussed in the context of emotion theories, and possible future applications and limitations are provided.

Complexity of cerebral blood flow velocity and arterial blood pressure in subarachnoid hemorrhage using time-frequency analysis

We investigated changes of time-frequency (TF) complexity, in terms of Rényi entropy and a measure of concentration, of middle cerebral blood flow velocity (CBFV) and arterial blood pressure in relation to the development of cerebral vasospasm in 15 patients after aneurysmal subarachnoid hemorrhage. Interhemispheric differences in the period of no vasospasm and vasospasm were also compared. Results show reduced complexity of TF representations of CBFV on the side of aneurysm before vasospasm was identified. This potentially can serve as an early-warning indicator of future derangement of cerebral circulation.

Oscillatory behavior of ventricular action potential duration in heart failure patients at respiratory rate and low frequency

Oscillations of arterial pressure occur spontaneously at a frequency of approximately 0.1 Hz coupled with synchronous oscillations of sympathetic nerve activity (“Mayer waves”). This study investigated the extent to which corresponding oscillations may occur in ventricular action potential duration (APD). Fourteen ambulatory (outpatient) heart failure patients with biventricular pacing devices were studied while seated upright watching movie clips to maintain arousal. Activation recovery intervals (ARI) as a measure of ventricular APD were obtained from unipolar electrograms recorded from the LV epicardial pacing lead during steady state RV pacing from the device. Arterial blood pressure was measured non-invasively (Finapress) and respiration monitored. Oscillations were quantified using time frequency and coherence analysis. Oscillatory behavior of ARI at the respiratory frequency was observed in all subjects. The magnitude of the ARI variation ranged from 2.2 to 6.9 ms (mean 5.0 ms). Coherence analysis showed a correlation with respiratory oscillation for an average of 43% of the recording time at a significance level of p < 0.05. Oscillations in systolic blood pressure in the Mayer wave frequency range were observed in all subjects for whom blood pressure was recorded (n = 13). ARI oscillation in the Mayer wave frequency range was observed in 6/13 subjects (46%) over a range of 2.9 to 9.2 ms. Coherence with Mayer waves at the p < 0.05 significance level was present for an average of 29% of the recording time. In ambulatory patients with heart failure during enhanced mental arousal, left ventricular epicardial APD (ARI) oscillated at the respiratory frequency (approximately 0.25 Hz). In 6 patients (46%) APD oscillated at the slower Mayer wave frequency (approximately 0.1 Hz). These findings may be important in understanding sympathetic activity-related arrhythmogenesis.

Comparative evaluation of methodologies for T-wave alternans mapping in electrograms

Electrograms (EGM) recorded from the surface of the myocardium are becoming more and more accessible. T-wave alternans (TWA) is associated with increased vulnerability to ventricular tachycardia/fibrillation and it occurs before the onset of ventricular arrhythmias. Thus, accurate methodologies for time-varying alternans estimation/detection in EGM are needed. In this paper, we perform a simulation study based on epicardial EGM recorded in vivo in humans to compare the accuracy of four methodologies: the spectral method (SM), modified moving average method, laplacian likelihood ratio method (LLR), and a novel method based on time-frequency distributions. A variety of effects are considered, which include the presence of wide band noise, respiration, and impulse artifacts. We found that 1) EGM-TWA can be detected accurately when the standard deviation of wide-band noise is equal or smaller than ten times the magnitude of EGM-TWA. 2) Respiration can be critical for EGM-TWA analysis, even at typical respiratory rates. 3) Impulse noise strongly reduces the accuracy of all methods, except LLR. 4) If depolarization time is used as a fiducial point, the localization of the T-wave is not critical for the accuracy of EGM-TWA detection. 5) According to this study, all methodologies provided accurate EGM-TWA detection/quantification in ideal conditions, while LLR was the most robust, providing better detection-rates in noisy conditions. Application on epicardial mapping of the in vivo human heart shows that EGM-TWA has heterogeneous spatio-temporal distribution.

Cardiorespiratory dynamic response to mental stress: a multivariate time-frequency analysis

Mental stress is a growing problem in our society. In order to deal with this, it is important to understand the underlying stress mechanisms. In this study, we aim to determine how the cardiorespiratory interactions are affected by mental arithmetic stress and attention. We conduct cross time-frequency (TF) analyses to assess the cardiorespiratory coupling. In addition, we introduce partial TF spectra to separate variations in the RR interval series that are linearly related to respiration from RR interval variations (RRV) that are not related to respiration. The performance of partial spectra is evaluated in two simulation studies. Time-varying parameters, such as instantaneous powers and frequencies, are derived from the computed spectra. Statistical analysis is carried out continuously in time to evaluate the dynamic response to mental stress and attention. The results show an increased heart and respiratory rate during stress and attention, compared to a resting condition. Also a fast reduction in vagal activity is noted. The partial TF analysis reveals a faster reduction of RRV power related to (3 s) than unrelated to (30 s) respiration, demonstrating that the autonomic response to mental stress is driven by mechanisms characterized by different temporal scales.

Cross time-frequency analysis for combining information of several sources: application to estimation of spontaneous respiratory rate from photoplethysmography

A methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error {0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error {4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration.